Model-based assessment of mammalian cell metabolic functionalities using omics data
Model-based assessment of mammalian cell metabolic functionalities using omics data
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DOI：
10.1016/j.crmeth.2021.100040
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发表时间：
2021-07-26
期刊：
影响因子：
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通讯作者：
Lewis, Nathan E.
中科院分区：
文献类型：
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作者：
Richelle, Anne;Kellman, Benjamin P.;Lewis, Nathan E.
组学实验在生物学研究中无处不在，导致数据泛滥。然而，由于基因、蛋白质和代谢物之间复杂的相互依赖关系，将这些数据的变化与细胞功能的变化联系起来仍然具有挑战性。在这里，我们提出了一个框架，允许研究人员推断代谢功能如何在组学数据的基础上变化。为了实现这一点，我们整理和标准化了哺乳动物细胞可以完成的代谢任务列表。基因组尺度的代谢网络被用来定义与每个代谢任务相关的基因集。我们进一步开发了一个框架，将组学数据叠加在这些集合上，并预测每个代谢任务的途径使用情况。我们展示了这种方法如何通过使用多个转录组数据集来量化从单细胞到整个组织和器官的各种生物样品的代谢功能。为了便于采用，我们将该方法集成到geneppattern （www.genepattern.org-CellFie）中。
Omics experiments are ubiquitous in biological studies, leading to a deluge of data. However, it is still challenging to connect changes in these data to changes in cell functions because of complex interdependencies between genes, proteins, and metabolites. Here, we present a framework allowing researchers to infer how metabolic functions change on the basis of omics data. To enable this, we curated and standardized lists of metabolic tasks that mammalian cells can accomplish. Genome-scale metabolic networks were used to define gene sets associated with each metabolic task. We further developed a framework to overlay omics data on these sets and predict pathway usage for each metabolic task. We demonstrated how this approach can be used to quantify metabolic functions of diverse biological samples from the single cell to whole tissues and organs by using multiple transcriptomic datasets. To facilitate its adoption, we integrated the approach into GenePattern (www.genepattern.org-CellFie).